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About This Role
Company Overview
Established in 2008, Zicasso is the most\-reviewed and highest\-rated luxury travel company, specializing in immersive custom tours, vacations, and safaris. Our Zicasso travel specialists are the world’s top 2% of boutique tour operators and travel agents, pre\-vetted and handpicked to compete in creating itineraries tailored to each of our travelers’ unique preferences. Joining Zicasso, you'll play a key role in bringing our travelers’ dreams to life—pushing the boundaries of what’s possible with luxury travel experiences.
As a member of our team, you’ll be part of a company recognized for its strong brand reputation among discerning travelers. Our authority in the luxury travel industry is consistently affirmed through features in top media outlets, including The New York Times, The Wall Street Journal, BBC, Forbes, CNN, and Condé Nast Traveler, to name a few. See our press coverage here.
We are a fully remote company spanning five continents, and we foster a dynamic, progressive global work environment that values creativity, initiative, and continuous learning. We're seeking passionate, data\-driven individuals who thrive in a high\-performance environment and are eager to contribute to our innovative company culture, underpinned by a consistent pursuit of excellence, integrity, and teamwork.
Our global team comes together biannually for international retreats and summits. These are typically held for a week at a new location each time, providing opportunities to share ideas, collaborate in person, and strengthen our culture, all while learning firsthand about the destinations we serve. These events embody our commitment to both professional growth and the transformative power of travel.
Join us in shaping the future of luxury travel while working towards our vision—creating a more connected humanity through travel. To learn more, visit https://www.zicasso.com/careers.
The Role
As a Marketing Data Scientist at Zicasso, you will have the opportunity to leverage your expertise in data analysis and modeling to extract valuable insights that will drive the company's strategic decision\-making. You will work closely with cross\-functional teams to develop and implement data\-driven solutions that improve operations, enhance customer experiences, and optimize business performance.
There is flexibility in your work hours but we expect that you will generally maximize your overlap with California hours.
The work will all be conducted in English.
Key Responsibilities
- Analyze large datasets to uncover trends, patterns, and insights that drive strategic business decisions.
- Develop and refine predictive models for customer segmentation, pricing optimization, and demand forecasting.
- Provide actionable data insights to support targeted marketing campaigns, customer retention strategies, and personalized communication.
- Partner with product, marketing, and customer experience teams to uncover opportunities for conversion and retention improvement.
- Apply advanced statistical methods and machine learning techniques to solve complex business problems and improve operational efficiency.
- Collaborate with data engineering to ensure data quality, consistency, and scalability across systems.
- Document methodologies and communicate findings clearly to both technical and non\-technical stakeholders.
- Contribute to fostering a data\-driven culture by sharing insights, tools, and best practices across teams.
Required Experience
- Bachelor's degree in Statistics, Mathematics, Computer Science, or a related field. Master or Ph.D. preferred.
- Proven experience as a Data Scientist in a fast\-paced and data\-driven environment.
- Expertise in statistical analysis, machine learning, and predictive modeling techniques.
- Proficiency in programming languages such as Python or R.
- Strong SQL skills and experience working with large\-scale datasets.
- Experience with data visualization tools such as Tableau or Power BI.
- Excellent problem\-solving skills and ability to translate business requirements into analytical solutions.
- Strong communication skills and ability to present complex findings to both technical and non\-technical stakeholders.
- Ability to thrive in a fully remote environment, working independently while maintaining close collaboration with cross\-functional teams.
- Minimum of 3 years of experience embedded within or consulting for a marketing department. Ability to translate complex statistical findings into actionable insights for non\-technical stakeholders
- Deep technical expertise in navigating and extracting data from the Google ecosystem (Google Ads, GA4, and Search Console). Proven track record of using BigQuery to join disparate marketing datasets for cross\-channel analysis and attribution modeling.
What We Offer
- Remote work from your home base and flexible hours that allow you to enjoy a great work\-life balance
- Innovative, fast\-paced and collaborative culture that values diverse voices and opinions
- Competitive compensation and benefits package (medical, dental, vision)
- Open Paid Time Off policy and 10 days of company\-paid holidays
- Stock options package
- 401K retirement plan with employer matching
- Substantially discounted luxury travel during off\-season
- Learning and development annual stipend
- Enjoy two company\-sponsored business trips each year at international destinations we serve!
All applications should be submitted in English.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Zicasso, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Zicasso AI Hiring
Zicasso has 1 open AI role right now. They're hiring across Data Scientist. Based in TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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